STAT212meanandvariance

# STAT212meanandvariance - Sample distribution of the sample...

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If X is continuous, the mean would be given by the center of its distribution. If X is discrete, the expected value would be sum of (X)(Px). The variance would be (x-u)^2 (Px). Correlation is denoted by Pxy. If X and Y are independent, then Pxy= 0. If there is an expression (a + bx), then the mean would be a + b(Ux). The variance would be b^2(Vx). If it is X+Y, the means and variances are added and if it is X-Y, the means are subtracted but the variances are still added (provided that they are independent). Variance rule for dependent variables: X+Y= Vx + Vy + 2(Pxy)(Sx)(Sy) and X-Y= Vx + Vy – 2(Pxy)(Sx) (Sy)
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Unformatted text preview: Sample distribution of the sample mean- If the population is normal, the sample mean x^= population mean u however variance of the sampling distribution gets divided by the sample size (or SD gets divided by the root of n). If the population is non-normal, the central limit thereom ensures that it will be normal, provided the sample size is large enough. Combined events- P(A and B)= P(A) + P(B) P(A and B) If two events are independent, we use the multiplication rule, P(A) x P(B) The mean of a random variable, unlike a set of observations, takes into account that all outcomes are not equally likely....
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## This note was uploaded on 02/27/2011 for the course STAT 2120 taught by Professor Jeffreyholt during the Fall '10 term at UVA.

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